Difference between Poisson and Binomial distributions. [closed]
If both the Poisson and Binomial distribution are discrete, then why do we need two different distributions?
closed as offtopic by Xander Henderson, Shaun, Namaste, user 170039, The Phenotype May 30 ’18 at 15:04
This question appears to be offtopic. The users who voted to close gave this specific reason:

“This question is missing context or other details: Please improve the question by prov >If this question can be reworded to fit the rules in the help center, please edit the question.
2 Answers 2
The Binomial and Poisson distributions are similar, but they are different. Also, the fact that they are both discrete does not mean that they are the same. The Geometric distribution and one form of the Uniform distribution are also discrete, but they are very different from both the Binomial and Poisson distributions.
The difference between the two is that while both measure the number of certain random events (or “successes”) within a certain frame, the Binomial is based on discrete events, while the Poisson is based on continuous events. That is, with a binomial distribution you have a certain number, $n$, of “attempts,” each of which has probability of success $p$. With a Poisson distribution, you essentially have infinite attempts, with infinitesimal chance of success. That is, given a Binomial distribution with some $n,p$, if you let $n\rightarrow\infty$ and $p\rightarrow0$ in such a way that $np\rightarrow\lambda$, then that distribution approaches a Poisson distribution with parameter $\lambda$.
Because of this limiting effect, Poisson distributions are used to model occurences of events that could happen a very large number of times, but happen rarely. That is, they are used in situations that would be more properly represented by a Binomial distribution with a very large $n$ and small $p$, especially when the exact values of $n$ and $p$ are unknown. (Historically, the number of wrongful criminal convictions in a country)